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A developmental stage-aware graph transformer framework for automated bone-age assessment.

Kerang Cao^1,2, Chang Liu^1,2, Jiaming Du^1,2

¹College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, China.

Quantitative Imaging in Medicine and Surgery

|January 12, 2026

Summary

This study introduces a novel framework for automated bone-age assessment, achieving high accuracy and interpretability. The developmental stage-aware graph transformer framework (DSGTF) improves pediatric growth monitoring and endocrine disorder diagnosis.

Keywords:

Bone age assessment deep learning developmental stage awareness graph neural networks (GNNs)transformer

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Area of Science:

Medical Imaging
Artificial Intelligence
Pediatric Endocrinology

Background:

Bone-age assessment is vital for pediatric growth monitoring and diagnosing endocrine disorders.
Traditional methods (Greulich-Pyle, Tanner-Whitehouse) are subjective, complex, and time-consuming.
Existing automated methods lack effective region highlighting and anatomical association capture.

Purpose of the Study:

To develop a novel framework for automated bone-age assessment integrating anatomical knowledge and developmental stage awareness.
To improve the accuracy and interpretability of automated bone-age assessment.
To address limitations of current manual and automated assessment techniques.

Main Methods:

A developmental stage-aware graph transformer framework (DSGTF) was developed.
The framework integrates image preprocessing, key region detection, and feature extraction.
A graph transformer architecture models anatomical relationships, enhanced by a developmental stage-aware module for adaptive processing.

Main Results:

The DSGTF model achieved a Mean Absolute Error (MAE) of 4.82 months.
Consistent performance was observed across different age groups and sexes (MAE 3.39-6.03 months).
The model demonstrated robustness to image transformations, with stable performance and minimal MAE increases.

Conclusions:

The DSGTF framework offers an accurate, efficient, and interpretable automated solution for bone-age assessment.
The approach shows strong stability and consistency, making it reliable for clinical applications.
The model dynamically adjusts attention to skeletal regions based on developmental stages, mimicking radiologist decision-making.